1. Technical Field
This invention relates to systems and methods for analyzing images, and more particularly, to system for locating features in an image.
2. Discussion
One of the primary goals of image processing is to automatically recognize certain features or textures within an image. In typical image processing systems, fields of picture elements (pixels) are automatically scanned, and some algorithm is chosen to analyze the image to recognize particular features. One approach is to arbitrarily select a given region, or window, in the image and to analyze the pixels within the window to detect the particular features. For example, one previous approach is to calculate a centroid, or weighted centroid, on all of the pixels within a window that are below a certain fixed threshold, where the desired feature is known to have an intensity below the threshold. This kind of approach, however, often causes adjacent objects, that are not sought to be detected, to add errors to the calculation when they fall below the threshold. In addition, often such thresholds must be set somewhat higher than desirable to account for variability in the desired features. This higher intensity threshold often can allow for even more errors from adjacent objects. Thus, it would be desirable to provide an image processor which is less prone to detect unwanted features adjacent to the desired features to more accurately analyze the desired features. In addition, it would be desirable to provide an image processor in which the threshold used to detect the features is adaptable to particular portions of the image, and not fixed. In this way the threshold may be set more closely approximate the intensity of the feature.
One image processing application of particular concern, is the task of automatically inspecting printed circuit boards. This task is made more complicated because the mechanical systems employed in the manufacture of printed circuit boards do not always position solder joints in exactly the same place every time. In addition, a the number of features of the board vary from board to board.
An early step in such automatic inspection systems is to analyze an image of the printed circuit board. For example, this may be an X-ray image. As a result of inaccuracies in manufacturing, when an automatic inspection system attempts to inspect a particular feature on the board, such as a solder joint, the physical appearance of the joint in the image does not match from one board to the next, or from one production run to the next. Thus, to accomplish automatic inspection of printed circuit boards, there is needed a way to find the physical position of the joint in an image accurately enough to perform all of the subsequent tests. If the feature, such as the solder joint is not accurately located, the subsequent testing will give faulty results.
For example, if the system is looking for an area that should have substantial amounts of solder but the test system is slightly offset so that it does not look at the center of the solder joint, the system may assume that there should be solder where it should not be and classify a good joint as a bad joint. Conversely, this problem may also cause the system to label a bad joint, a good joint.
Thus, it would be desirable to provide an image processing system which can accurately find the center of features on a printed circuit board, such as solder joints, to improve the accuracy of automatic testing systems.